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Extra resources for Statistical and Neural Classifiers: An Integrated Approach to Design

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Complex MLP classifiers can have many hidden layers. Fig. 4. MLP with one hidden layer. 8 Fig. 5. 30 bivariate training vectors and decision boundaries of MLP with 10 hidden units and hard-limiting (1) and smooth (2) activation functions. 10 1. 7). In the gradient descent optimisation algorithm, we need to calculate derivatives of the cost function and update the weights for all neurones in an iterative manner similar to the training of the SLP. While calculating the gradients of the hidden layer neurones, we propagate the error signal t ji) -f (V T X y) + vo) back to the lower layers.

E. initially transforming the data into spherical data. Then we have to move the data centre to the zero point and begin training with zero initial weights. So, after the first iteration we can obtain the EDC, which is a very good initial classifier for spherical data. In further training, we can utilise all the positive properties of the neural network approach. This allows us to obtain comparatively good classification rules if the training-set size is small or the pattern-class models are non-Gaussian.

Hence, for the GCCM data model we have that E:' ~ E~. Note that the Bayes error does not depend on the particular classifier design method. Rather, it depends on the characteristics of the data model. Example 4. 49700, ... 0326l. 4 Generalisation Error of EDC and Fisher LDF variances. 01. In this book, we refer to this data configuration as data model C. 21), respectively, as well as numerical calculation show that the asymptotic errors depend on the type of classifier design method. Both the EOC and the Fisher OF are linear classifiers.